Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces (2109.00162v4)

Abstract: Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones. In this work, we show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces and further describe an automatic method to extract the pupils from two eyes and analysis their shapes for exposing the GAN-generated faces. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN-generated faces.

Analysis of "EYES TELL ALL: IRREGULAR PUPIL SHAPES REVEAL GAN-GENERATED FACES"

The paper "EYES TELL ALL: IRREGULAR PUPIL SHAPES REVEAL GAN-GENERATED FACES" presents a novel approach for identifying GAN-generated faces through the analysis of pupil shape regularity. The authors, Hui Guo et al., propose a method that exploits irregularities in the shape of pupils generated by certain GAN models, an artifact arising from the lack of physiological constraints in their architecture. This method is designed using a straightforward segmentation of pupil shapes followed by an analysis to determine whether they deviate significantly from expected circular or elliptical geometries.

Summary of Work

Methodology

The paper describes several key steps involved in their detection pipeline:

1. Pupil Segmentation: Initially, the researchers apply automated techniques to segment pupil regions within the eye. This segmentation isolates boundaries which will be analyzed for regularity.
2. Ellipse Fitting: An ellipse is fit to the segmented pupil shape using least-square fitting, constrained to ensure logical results avoiding trivial solutions. The focus is exclusively on the outer boundary of the pupil.
3. Irregularity Measure: To quantify the irregularity of the pupil shape, they calculate Boundary Intersection-over-Union (BIoU) scores. BIoU focuses on pixel alignment near the boundary, a necessary consideration given the paper's reliance on matching accurate shape contours rather than internal features.

Key Findings

The analysis showed stark differences between the BIoU scores for real and GAN-generated faces. Pupils of real human eye images regularly produced high BIoU scores, indicative of their geometric regularity. Conversely, GAN-trained models, such as StyleGAN2 and others, generated faces with lower BIoU scores due to irregular, inconsistent pupil shapes. The ROC curve derived from these scores provides compelling evidence of the method's efficacy, achieving an area under the curve (AUC) score of 0.91.

Contributions and Implications

This work makes two principal contributions. First, it pioneers using pupil shape consistency as an indicator for detecting GAN-generated fake faces, which until now had relied on exploiting other less universal facial artifacts. Secondly, the method is presented as both effective and interpretatively clear, relying on physiological cues that humans naturally recognize.

The implications of this research are noteworthy both practically and theoretically. Practically, it provides a new robust tool for image forensics, especially in combating misuse of synthetic media in social platforms and other domains where identity verification is critical. Theoretically, it suggests an avenue for improving GAN models by better integrating physiological constraints to mitigate these detectable artifacts.

Future Developments

While the paper achieves promising results, it inevitably opens up questions for future exploration. There is potential to refine this method by exploring more complex inconsistencies or augmenting pupil detection with additional facial features. Additionally, extending this paradigm might support detection against more advanced GAN models as they continue to evolve. Similarly, techniques to integrate this detection into real-world applications and platforms could be pursued, providing wider access to these forensic tools. The authors acknowledge the challenge posed by visual conditions and suggest that further studies should aim to address false positives that arise from atypical or obscured real-world pupil images.

Conclusion

The paper establishes a clear, novel path in the forensics of digital media, leveraging observable inconsistencies in GAN-generated data. By focusing on a fundamental physiological feature like the pupil, it not only aids in reliable detection but also challenges future GAN architecture designs towards more nuanced, truly indistinguishable imagery synthesis. This work thus stands as an essential contribution to the ongoing efforts in the field of digital image forensics.